The drinking water of a German city was investigated for microplastics. Random samples were taken from three house connections, one transfer station, and five consumption taps in an educational institution, an apartment, a single-family house, a residential building, and a commercial enterprise. The sample volumes ranged from 0.25-1.3 m3. A stainless-steel membrane served as a sampling filter. HCl was used for the acidic digestion of the inorganic precipitates. Raman micro spectroscopy was used for the analysis of microplastic particles. The detection limit was 10 μm. Particles and fibers ≥100 μm were measured on 100% of the filter area, while particles and fibers <100 μm were measured on 50% of the filter area. The analytical procedure was validated by determining recovery rates and analyses of blanks. In addition to microplastics, pigmented particles and fibers were also analyzed. Neither microplastic particles and fibers nor pigmented particles and fibers were detected at house connections and the transfer station. No microplastics were detected in any sample at consumption taps. At one tap, 113 ± 83 copper phthalocyanine particles m-3 were detected.

In this study, we investigate the current accuracy of widely used microplastic (MP) detection methods through an interlaboratory comparison (ILC) involving ISO-approved techniques. The ILC was organized under the prestandardization platform of VAMAS (Versailles Project on Advanced Materials and Standards) and gathered a large number (84) of analytical laboratories across the globe. The aim of this ILC was (i) to test and to compare two thermo-analytical and three spectroscopical methods with respect to their suitability to identify and quantify microplastics in a water-soluble matrix and (ii) to test the suitability of the microplastic test materials to be used in ILCs. Two reference materials (RMs), polyethylene terephthalate (PET) and polyethylene (PE) as powders with rough size ranges between 10 and 200 μm, were used to press tablets for the ILC. The following parameters had to be assessed: polymer identity, mass fraction, particle number concentration, and particle size distribution. The reproducibility, S_R, in thermo-analytical experiments ranged from 62%-117% (for PE) and 45.9%-62% (for PET). In spectroscopical experiments, the S_R varied between 121% and 129% (for PE) and 64% and 70% (for PET). Tablet dissolution turned out to be a very challenging step and should be optimized. Based on the knowledge gained, development of guidance for improved tablet filtration is in progress. Further, in this study, we discuss the main sources of uncertainties that need to be considered and minimized for preparation of standardized protocols for future measurements with higher accuracy.

The elimination of microplastic particles (MPP) and microplastic fibers (MPF) was investigated in the final treatment stage (sand filtration) in two municipal wastewater treatment plants (WWTP) and the final treatment stage (sand filtration) at a process WWTP of a manufacturer of polyvinyl chloride (PVC). At each sampling site, three samples on three different days were taken (before/after sand filtration). The samples were filtered through a 10 μm stainless steel cartridge filter utilizing a stainless steel centrifugal pump. Microplastics (MP) were separated from the wastewater matrix by oxidative treatment and density separation and analyzed by Raman microspectroscopy. Due to precautionary measures, procedural blanks were very low with a mean number of 4.3 ± 2.7 MPP and 0.88 ± 0.56 MPF within eight blank samples. The municipal WWTPs were able to eliminate 99.2% ± 0.29% and 99.4% ± 0.15% of MP in the sand filtration stage. The sand filtration of a PVC manufacturer eliminated 99.2%–99.9%.

This research project investigates the potential of machine learning for the analysis of microplastic Raman spectra in environmental samples. Based on a data set of > 64,000 Raman spectra (10.7% polymer spectra) from 47 environmental or waste water samples, two methods of deep learning (one single model and one model per class) with the Rectified Linear Unit function (ReLU) (hidden layer) as the activation function and the sigmoid function as the output layer were evaluated and compared to human-only annotation. Based on the one-model-per-class algorithm, an approach for human–machine teaming was developed. This method makes it possible to analyze microplastic (polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) spectra with high recall (≥ 99.4%) and precision (≥ 97.1%). Compared to human-only spectra annotation, the human–machine teaming reduces the researchers’ time required per sample from several hours to less than one hour.

This study is one of the first to investigate the recovery rate of high- and low-density microplastic particles (polyvinyl chloride and polypropylene) from wastewater treatment plant effluents or comparable technical facilities under nearly realistic experimental conditions. For this purpose, a method of continuous dosing of microplastic particles into an experimental flume for open-channel flow was developed. Subsequently, 12 samples were taken using volume-reduced sampling and the entire sample purification process including oxidative treatment (with hydrogen peroxide and sodium hypochlorite), density separation (with sodium polytungstate), and subsampling was carried out. Detection was conducted using automatic particle recognition and µ-Ramanspectroscopy. An average recovery rate of 27 ± 10% was determined for polypropylene microplastic particles (d = 53 ± 29 µm), decreasing with the particle size, and 78 ± 14% for polyvinyl chloride microplastic particles (d = 151 ± 37 µm). The results suggest that microplastic emissions from wastewater treatment plants are underestimated, particularly because the recovery rate of small microplastic particles < 50 µm is only 9%.

Although the paper industry processes polymeric materials and discharges large amounts of wastewater, no research on microplastics in the wastewater from paper mills has been published to date. This study is the first to investigate this issue. The wastewater treatment plants of twelve representatively selected German paper mills were investigated using an analysis protocol based on µ-Raman spectroscopy. The results show that treated process water from surface waters is negligible as a source of microplastics (MPs) ≥ 20 µm. The microplastics concentrations in untreated wastewater range from 10⁶ to 10⁸ (MPs ≥ 20 µm)/m³. Sources of microplastics in wastewater include recovered paper, functional polymers, and coating colors, among others. The most frequently detected polymers are polyethylene and polystyrene. In four cases, moving bed biofilm reactors were identified as a source of microplastics. The microplastics concentration in treated wastewater ranges from 10² to 10⁴ (MPs ≥ 20 µm)/m³. Hence, the removal rate of the wastewater treatment plants exceeds 99 %. Mechanical treatment and the activated sludge process have the highest removal rates of all treatment stages. The loads emitted into surface waters range from 10⁶ to 10⁸ (MPs ≥ 20 µm)/h, comparable to municipal wastewater treatment plants with a population equivalent of over 10,000 inhabitants. Compared with other wastewater-related emissions (the total emissions of municipal wastewater treatment plants, or combined sewer overflow), the contribution of paper mills to microplastics in the aquatic environment is low. The results of the removal efficiency can be transferred to other branches of industry and municipal wastewater treatment plants.